what happen aya naon

Get Link

Digital spatial data sets have grown rapidly in scope, coverage, and volume over the last decade. We have moved from data-poverty to data-ichness. On the other hand, environmental models have steadily grown more complex, are more frequently used, are expected to deal with larger data volumes, and give better predictions over a wider range of issues from local to global scales. The abundance of digital data is leading to its own set of problems in identifying and locating relevant data and in evaluating choices of resolution, coverage, provenance, cost, and conformance with the models to be used. Furthermore, for environmental models it may not be so much the characteristics of the raw data that are the most critical, but their characteristics once converted, aggregated, and implemented in the model. Given that a modeling task may access data from multiple sources, there is the added difficulty of assessing combined performance in relation to the implementation of the simulati…